In adaptive resonance theory (ART) networks, an overabundance of neurons leads some neurons to be committed (active) and others to be uncommitted (inactive). The weight vector, also known as the prototype, is said to resonate with the input vector if the two are sufficiently similar. Weights are only updated if they are resonating in the current iteration. ART networks commit an uncommitted neuron when a new input pattern is detected that does not resonate with any of the existing committed neurons. ART networks are fully-connected networks, in that all possible connections are made between all nodes.